AI tools for data analytics, compared

Hex is a notebook environment with AI built into the cells rather than bolted on as a chat window. You describe what you want, it writes the SQL or Python into a cell, and the cell runs against your warehouse. The output you keep is the code, not a conversation.

That distinction matters more than any feature comparison. A number produced in a chat window is gone when the chat scrolls away; a number produced by a notebook cell can be rerun, diffed and handed to a colleague. If you have ever tried to reconstruct where a figure in a deck came from, this is the difference between an afternoon and a minute.

The tradeoff is that it expects a warehouse and a team. If your data lives in spreadsheets and you are working alone, the setup cost is real and the payoff is small.

Upload a file, ask a question, and it writes Python, executes it, and describes the result. The important part is that the arithmetic happens in the sandbox, not in the model. Ask it to sum a column and you get a real sum.

For exploring a dataset nobody documented, this is the fastest option on the list. Ten minutes of asking what is in a file replaces an hour of scrolling through columns.

Two cautions. Expand the code block and read it, because the code is where a wrong assumption becomes visible and the summary sentence is where it becomes invisible. And the file goes to a third party, which is a procurement question before it is a technical one.

Copilot sits on top of the semantic model you have already built, which is the single biggest reliability advantage available. When "revenue" is defined once as a measure, the AI cannot invent a second definition of it.

This cuts both ways, and it is worth being blunt about. A well-governed model makes Copilot look excellent. A model with four half-finished date tables and ambiguous relationships makes it look useless, and the fault is not the AI. Most disappointing rollouts we hear about are model problems wearing an AI costume.

It also stays inside your tenant, which resolves most of the data governance objection to the chat tools above.

Coupler.io is a reporting automation platform first: scheduled data flows pull from several hundred sources into a destination like BigQuery, Sheets or Looker Studio. The AI part is what it does with those flows once they exist.

Its MCP server gives an AI assistant a read-only query interface to the tables you have already synced. That shape solves the main objection to the chat tools higher up this page. You are not uploading a customer export to a third party and hoping; the data stays put and the model sends queries to it. You still get the flexibility of asking questions in plain language, and the query it ran is visible in the conversation.

There is also an AI Insights feature inside its dashboards that summarises what a report is showing, which is the lighter-weight option for people who are never going to open a query interface.

The tradeoff is that this is only useful if the pipeline part earns its place independently. You are maintaining data flows, and answer quality still depends on how sensibly the synced tables and columns are named. For a one-off question about a CSV on your desktop it is the wrong tool entirely.

Functionally close to ChatGPT for this use: upload a file, get code written and executed, read the summary. The strengths and the cautions are the same ones, including the fact that the file leaves your environment.

Worth testing both on your own data rather than taking anyone else's word on which is better, since results vary by file shape and by question. We would say the same about any pairing on this list.

The output is a formula in a cell, which means it is inspectable by anyone who opens the workbook. For spreadsheet work that is a better audit trail than a chat transcript.

It is also the tool most affected by how your sheet is laid out. Merged cells, a title above the headers, subtotals in the middle of the data: all the things that make a sheet readable to humans make it harder for the AI. Clean the layout first and the results change noticeably.

The Sheets equivalent of the above, and the reasoning is identical. Useful for formulas, categorisation and quick summaries. Constrained by the same layout problems.

Best treated as formula assistance rather than an analysis engine. For anything involving real computation over many rows, verify against a second method before you use the number.

A conversational layer over data you have already modelled and governed. It generates SQL, runs it, and shows you the query, which is the property that separates a usable agent from an unusable one.

Agents chain steps, and chained steps compound errors. Being able to read the query at each step is what makes that acceptable rather than alarming. Anything in this category that hides its queries should be treated as untrustworthy by default.

Designed for the case where many non-analysts need to ask questions of the same governed dataset. Drill-down from an answer to the rows behind it is the feature that matters most here, because it lets a sceptical reader check a number without asking you.

Like every tool in the built-in and agent groups, it is only as good as the model beneath it, and it does nothing for a spreadsheet somebody emailed you this morning.